Bibliographic record
Abstract
This writing explores glamour as a phenomenon of performance, arguing that through performance glamour becomes an epistemological impulse that creates both knowledge and remains of femininities, among other forms of identity and expression. Beginning with thorough definitions that integrate glamour and performance, this writing explores new theoretical and philosophical models that incorporate the realms of experience and eroticism in which an analysis of glamour may be approached, highlighting some of the polemics and dangers along the way. Departing from the traditional material cultural analysis of glamour that relegates it largely to patterns of consumption in the 20th century or to the Hollywoodian sphere of influence, this paper extends the temporality and influence of glamour indefinitely, taking glamour seriously an embodied practice deeply associated with the diverse forms and expressions of the feminine using Amanda Gorman’s Esteé Lauder campaign image as a closing example. Above all, this research takes glamour seriously, “calling foul on the strange anxieties about artifice, illusion, and performance, and the even more suspiciously gendered dismissal of glamour as cultural ‘fluff’ given that it is an embodied practice deeply associated with the feminine.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.119 | 0.038 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".